Filali Ben Sidel Farah, Bouziane Hassan, Del Mar Trigo Maria, El Haskouri Fatima, Bardei Fadoua, Redouane Abdelbari, Kadiri Mohamed, Riadi Hassane, Kazzaz Mohamed
Laboratory of Diversity and Conservation of Biological Systems, Faculty of Sciences, Mhannech II, University Abdelmalek Essaâdi, Postal Code 2121, Tetouan, Morocco.
Int J Biometeorol. 2015 Mar;59(3):339-46. doi: 10.1007/s00484-014-0845-1. Epub 2014 May 21.
Alternaria is frequently found as airborne fungal spores and is recognized as an important cause of respiratory allergies. The aerobiological monitoring of fungal spores was performed using a Burkard volumetric spore traps. To establish predicting variables for daily and weakly spore counts, a stepwise multiple regression between spore concentrations and independent variables (meteorological parameters and lagged values from the series of spore concentrations: previous day or week concentration (Alt t - 1) and mean concentration of the same day or week in other years (C mean)) was made with data obtained during 2009-2011. Alternaria conidia are present throughout the year in the atmosphere of Tetouan, although they show important seasonal fluctuations. The highest levels of Alternaria spores were recorded during the spring and summer or autumn. Alternaria showed maximum daily values in April, May or October depending on year. When the spore variables of Alternaria, namely C mean and Alt t - 1, and meteorological parameters were included in the equation, the resulting R (2) satisfactorily predict future concentrations for 55.5 to 81.6 % during the main spore season and the pre-peak 2. In the predictive model using weekly values, the adjusted R (2) varied from 0.655 to 0.676. The Wilcoxon test was used to compare the results from the expected values and the pre-peak spore data or weekly values for 2012, indicating that there were no significant differences between series compared. This test showed the C mean, Alt t - 1, frequency of the wind third quadrant, maximum wind speed and minimum relative humidity as the most efficient independent variables to forecast the overall trend of this spore in the air.
链格孢菌常以空气传播的真菌孢子形式存在，被认为是引起呼吸道过敏的重要原因。使用伯卡德体积式孢子捕捉器对真菌孢子进行气传生物学监测。为了确定每日和每周孢子计数的预测变量，利用2009 - 2011年期间获得的数据，对孢子浓度与自变量（气象参数以及孢子浓度序列的滞后值：前一天或前一周的浓度（Alt t - 1）以及其他年份同一天或同一周的平均浓度（C均值））进行逐步多元回归分析。在得土安的大气中，全年都有链格孢分生孢子存在，不过它们呈现出明显的季节性波动。链格孢孢子的最高水平出现在春季和夏季或秋季。根据年份不同，链格孢在4月、5月或10月出现每日最高值。当将链格孢的孢子变量（即C均值和Alt t - 1）以及气象参数纳入方程时，所得的R²在主要孢子季节和峰值前2期间能够令人满意地预测未来浓度的55.5%至81.6%。在使用每周数据的预测模型中，调整后的R²在0.655至0.676之间变化。采用威尔科克森检验来比较预期值与2012年峰值前孢子数据或每周数据的结果，表明所比较的序列之间没有显著差异。该检验表明，C均值、Alt t - 1、风的第三象限频率、最大风速和最小相对湿度是预测空气中这种孢子总体趋势的最有效自变量。
